In the next experiment, we compared embeddings generated by the CRDM with STDP as a learning rule to embeddings generated by the model without STDP (Fig. REF ). STDP is a plausible biological algorithm that adjusts the strength of synaptic associations between connected neurons. The process adjusts synaptic weights based on the timing of arriving signaling events (see for example [1]}). Starting with the same underlying geometric structure of a network (c.f section REF ), we randomly selected sets of 1000 instances of MNIST images of class 1 and 5 as input stimuli. With STDP enabled, we stimulated the model with the 1000 instances of images. We then took a snapshot of the graph with the updated weights, \(G_{STDP}\) . When we compared \(G\)  to \(G_{STDP}\)  we observed that 29% of the edges had higher weights, 40% of edges had lower weights, 20% became inhibitory, and 11% of the edge weights were unchanged. We then ran two sets of simulations, both with a single additional stimulus of either class 1 or 5; one using graph \(G\)  and one with graph \(G_{STDP}\) . We then extracted the dynamic paths from these additional stimulations and embedded them using the same methods as described in section REF . Using the same unsupervised kNN method, we inferred stimuli class labels. For embeddings generated with the dynamic paths without STDP, we achieved 61% accuracy separating out the two input classes while for dynamic paths with STDP as a learning rule, we achieved 82% accuracy. By comparison, if we trained a support vector machine (SVM), we were able to achieve 91.1% accuracy with embeddings from non-STDP paths and 97.6% for those with STDP.
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